# Aspect-Based Summarization with Self-Aspect Retrieval Enhanced Generation

arXiv: https://arxiv.org/abs/2504.13054 · HTML: https://mlsanity.com/paper/2504.13054/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 28 of 28)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [∞Bench: Extending Long Context Evaluation Beyond 100K Tokens](https://mlsanity.com/paper/2402.13718/) (2024)
- [LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models](https://mlsanity.com/paper/2309.12307/) (2023)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [Compressing Context to Enhance Inference Efficiency of Large Language Models](https://mlsanity.com/paper/2310.06201/) (2023)
- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization](https://mlsanity.com/paper/2302.08081/) (2023)
- [To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis](https://mlsanity.com/paper/2305.13230/) (2023)
- [Multi-Dimensional Evaluation of Text Summarization with In-Context Learning](https://mlsanity.com/paper/2306.01200/) (2023)
- [AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge](https://mlsanity.com/paper/2412.13670/) (2024)
- [Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning](https://mlsanity.com/paper/2211.12878/) (2022)
- [ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://mlsanity.com/paper/2110.08296/) (2021)
- [On Context Utilization in Summarization with Large Language Models](https://mlsanity.com/paper/2310.10570/) (2023)
- [OASum: Large-Scale Open Domain Aspect-based Summarization](https://mlsanity.com/paper/2212.09233/) (2022)
- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [Can Textual Gradient Work in Federated Learning?](https://mlsanity.com/paper/2502.19980/) (2025)
- [Are LLMs Good Zero-Shot Fallacy Classifiers?](https://mlsanity.com/paper/2410.15050/) (2024)
- [LLaMandement: Large Language Models for Summarization of French Legislative Proposals](https://mlsanity.com/paper/2401.16182/) (2024)
- [LexAbSumm: Aspect-based Summarization of Legal Decisions](https://mlsanity.com/paper/2404.00594/) (2024)
- [USB: A Unified Summarization Benchmark Across Tasks and Domains](https://mlsanity.com/paper/2305.14296/) (2023)

## Built on by (most cited first, 3 of 3)

- [CARPAS: Towards Content-Aware Refinement of Provided Aspects for Summarization in Large Language Models](https://mlsanity.com/paper/2510.07177/) (2025)
- [OrchMAS: Orchestrated Reasoning with Multi Collaborative Heterogeneous Scientific Expert Structured Agents](https://mlsanity.com/paper/2603.03005/) (2026)
- [DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models](https://mlsanity.com/paper/2610.18635/) (2026)

## Abstract (arXiv)

Aspect-based summarization aims to generate summaries tailored to specific aspects, addressing the resource constraints and limited generalizability of traditional summarization approaches. Recently, large language models have shown promise in this task without the need for training. However, they rely excessively on prompt engineering and face token limits and hallucination challenges, especially with in-context learning. To address these challenges, in this paper, we propose a novel framework for aspect-based summarization: Self-Aspect Retrieval Enhanced Summary Generation. Rather than relying solely on in-context learning, given an aspect, we employ an embedding-driven retrieval mechanism to identify its relevant text segments. This approach extracts the pertinent content while avoiding unnecessary details, thereby mitigating the challenge of token limits. Moreover, our framework optimizes token usage by deleting unrelated parts of the text and ensuring that the model generates output strictly based on the given aspect. With extensive experiments on benchmark datasets, we demonstrate that our framework not only achieves superior performance but also effectively mitigates the token limitation problem.